CAN INCENTIVE-COMPATIBLE VALUE ELICITATION METHODS HELP DESIGN
PES SCHEMES?
Evidence from lab and field
Daan van Soest, Ty Turley, Paul Christian,
Rahel Kitessa, Eline van der Heijden
FEBRUARY 1, 2017
TILBURG UNIVERSITY
Contents
1. Introduction ............................................................................................................................................. 2
2. Field Experiment ....................................................................................................................................... 4
2. 1 Background ........................................................................................................................................ 4
2. 2 Conceptual Framework ...................................................................................................................... 5
2.3. Methods and Study Design ................................................................................................................ 6
2. 3.1 Uniform price Auction ................................................................................................................ 6
2.3.2 The Study Context ....................................................................................................................... 7
2.3.3 Sample Size Determination and Power Estimation ......................................................................... 8
2.3.4 Design and Implementation of Field Experiment ........................................................................ 9
2.4. Result of Field Experiment .............................................................................................................. 12
2.4.1 The Sample ................................................................................................................................ 12
2.4.2 Experimental Result: The First Task ......................................................................................... 13
2.4.3 Main Experimental Result: Tree Planting .................................................................................. 14
Conclusion .............................................................................................................................................. 18
3. Laboratory Experiment ........................................................................................................................... 19
3.1 Introduction ....................................................................................................................................... 19
3.2 Design of Laboratory Experiment..................................................................................................... 20
3.2.1 Set up of Laboratory Experiment ............................................................................................... 20
3.2.2 Procedures of Laboratory Experiment ....................................................................................... 20
2. 3. Result: Laboratory Experiment ....................................................................................................... 22
Conclusion .............................................................................................................................................. 27
Reference .................................................................................................................................................... 27
Appendix ..................................................................................................................................................... 29
1
1. Introduction
Payment for Environmental Services (PES) is a method that pays low cost environmental goods
and service providers (Wunder, 2015). Targeting such providers allows efficient allocation of
resources (Ferraro, 2008). Hence, success of PES depends on proper identification of low cost
service providers. This requires careful design of PES implementation tools to answer policy
relevant questions, which is usually the price (Berry et al., 2015). Different PES implementation
instruments are utilized to determine the payment level. The most widely utilized instrument is
posted offer take-it-or-leave-it (TILI) method. While TILI is easy to implement and does not
discriminate among the service providers (thus less controversial), it does not eliminate the
information rent. Information rent arises when the service providers face no incentive to reveal
their true costs (Ferraro, 2008).
Hence, an alternative instrument suggested to address the information rent problem in PES
implementation is the auctions, of which the sealed bid auctions are most popular (Ferraro, 2008,
Jack, 2013). Sealed bid auction was first conceived as an incentive compatible tool in
implementation of contracts and allocation of resources (Vickrey, 1961). Following this, literature
have increasingly utilized the variants of sealed bid auction such as, uniform price procurement
auction (UPA) and discriminatory procurement auctions (Noussair and Van Soest, 2014, Jack, 2013,
Jindal et al., 2013). In the procurement (reverse) auctions, there are many sellers and one buyer,
hence a role reversal compared to the conventional auctions.
Closely related to UPA is Becker–DGroot–Marschak (BDM) mechanism (Becker et al., 1964).
The property of BDM that differs it from UPA is that the price of a product is randomly drawn
and pre-determined, which allows determination of winners. Hence, BDM is also a dominant
strategy that induces truthful revelation of one’s optimal strategy (true value) because bidders
cannot influence price of the product (Latacz‐Lohmann and Hamsvoort, 1998). Comparable with
UPA, overstating more than optimal strategy will not profit bidders because; i) payment for the
product does not necessarily equal to the submitted bids ii) and if the over-stated amount is more
than predetermined price the bidder runs into a risk of not selling the product even though it was
optimal to sell. In the same way, under-stating below the optimal strategy to win will not profit
the bidder, as the bidder might run into the risk of selling the product with loss. Detail of auction
mechanism is discussed in the next section 2 below.
2
Therefore in addition to the truthful revelation advantage, BDM enables a simple and direct
derivation of the supply function without parametric estimation. Driving the supply function
using TILI is possible, but requires sample size which might be prohibitively large and expensive
compared to what is required in BDM (Berry et al., 2015). Taking into account the above
advantages of both mechanisms, but also the shortcoming of TILI, this paper attempts to
examine if UPA can be utilized to inform the price that can be used in TILI implementation.
We are not the first to study whether theoretically equal outcomes under UPA and TILI are
indeed the same in practice. Studies have recorded variance in output delivered (fraction of sell
for instance) by uniform price procurement auctions and TILI (Berry et al., 2015, Jack, 2013, Cason
and Plott, 2014). However, the attempt to explain the cause of variance does not result in detail or
conclusive explanations. Cason and Plott (2014) attributed the cause of variance between the two
mechanisms to misconception of unfamiliar mechanism by the subjects which results in mistakes
and they ruled out the role of preference as the cause. Berry et al. (2015) find risk aversion as the
cause of the variance, while Jack (2013) study does not directly deal with the causes of such
disparity.
In this study, we examine the mechanisms for variance between UPA and TILI. To this end,
depending on the discussion and hint by the Jindal et al. (2013) we hypothesis that there is less
deliberate decision making in TILI than in UPA. Two hypotheses are tested to support deliberate
choice hypothesis. First, the observables and behavioral parameters predict propensity to sell in
UPA but not in TILI. Second, more time is needed to make decision in the UPA than in TILI.
In attempt to address these objectives, we implement both field and lab experiment in which
subjects sell an item using UPA and TILI. Field experiment (see section 2) is conducted with
Ghanaian farmers while lab experiment is conducted with Tilburg university students. Both field
and lab experiment find that the fraction of an item sell (self-selecting into a programme) in the
UPA group is lower than in the TILI. This difference is statistically significant. Further
examination suggests that the decision maker’s preferences in UPA group predict the subjects
bidding behavior, allowing for auction to provide more information on decision implemented.
Whereas in the TILI group, the preferences and socio economic characteristics seem not so
important. However, it is find that lower time is spent on decision making in TILI than in
auction.
3
2. Field Experiment
2. 1 Background
Deforestation is estimated to contribute 12-15% of human induced greenhouse gas emission
(GHG) and one third of this emission happens in tropics (Chaplin-Kramer et al., 2015). The
alarming rate at which forest is being cleared in tropics adds to the urgency of prudent
intervention. In Africa for instance, 23 percent of the land formerly covered by forest was
converted to other land uses only between 1990 and 2010 (FAO, 2012). Whereas 70 percent of
GHGs emissions in Africa is caused by deforestation, resulting in negative externalities
elsewhere as well (Gibbs et al., 2007). As a part to address these concerns, forest conservation is
formally considered as an important element in tackling environmental problems at Paris COP12
decision (Ruth, 2016).
One of the practical forest conservation tool is Payment for environmental services (PES)
(Laurance, 2007, Blom et al., 2010, Wunder, 2012, Engel et al., 2008). PES is defined by
Wunder (2015) as a i) voluntary transactions, ii) between service users, and service providers,
iii) of well-specified environmental service iv) that are conditional on agreed rules of natural
resource management for generating offsite services.
While PES is an ideal tool for conservation in theory, practical determination of payment level
faces the classic problem of limited market for environmental goods and longer time required to
deliver environmental output (Jack, 2010). A classical problem of identifying the opportunity
cost thereby payment level for environmental goods and service providers is based on the
assumptions of (i) participation constraint, that is at least the cost of all service providers is
covered and (ii) incentive compatibility constraint, that service providers are matched with
contracts that is intended for their type (in terms of cost of conservation faced) (Ferraro, 2008).
Based on these assumptions, auction is advocated as an opportunity cost elicitation method to
identify low opportunity cost providers (Ferraro, 2008, Jindal et al., 2013, Engel et al., 2008). In
addition, auctions aggregate information which allows the provision of the entire supply
schedule of service providers (Jack, 2013, Vickrey, 1961).
We implement UPA and contrast the result with TILI in Ghana which is a country under high
risk of deforestation. The current study differs from previous study on the ground that it attempts
4
to address how behavioral factors might affect targeting schemes. It specifically attempts to
answer the following research questions.
i) Do theoretically equal UPA and TILI mechanisms result in the same initial allocation?
ii) What are the role of preferences on UPA and TILI mechanisms?
2. 2 Conceptual Framework
Consider the following case of valuation problem where farmers are required to plant trees for
compensation. Valuing how much to compensate for keeping trees alive is not straight forward.
Because the trees have private benefit to seller in many forms including fruits, shades, and
related consumption. Thus, a farmer who own land, take into account these benefits into his own
utility maximization and ignore additional benefit the trees provide to the society. Suppose now
that the farmers were given an incentive to do an extra plantation beyond the usual production
say (q*) with price of (p*). The economic problem is to answer what additional compensation
say (α), make the farmer indifferent with original utility say profit of (π*) for extra efforts and
cost incurred? Then new compensation can be calculated by expected change in profit before and
after plantation of trees (π’- π*).
If Π* = p*q* - c (q*), q* solves p* = (dc/dq); where c (q*) is cost at q*
Π’ = A - c (q’),
A = c (q’) + [p*q* - c (q*)]; where c (q’) is cost at q’ and A, the additional
benefit from planting trees
Change in profit yields:
Π’- Π* = A
Thus theoretically participants are willing to plant trees with A ≥ 0 then,
A ≥ c (q’) + [p*q* - c (q*)]
Rearranging gives as:
p* ≤ [A+ c (q*) - c (q’)]/q*………………………………………………………….(1)
Participant’s minimum willingness to accept vary with cost incurred to plant the trees and
inversely with quantity of trees to be planted. Thus a farmer optimal decision making of
determining how much to accept (Willingness to accept), depends on opportunity cost incurred.
Eliciting willingness to accept using both TILI and UPA are expected to yield the same results of
the participants minimum willingness to accept (Jack, 2010, Vickrey, 1961).
5
2.3. Methods and Study Design
2. 3.1 Uniform price Auction
Theoretically auction can be utilized as an instrument that incentivize reporting of unbiased
marginal costs (marginal benefits) (Vickrey, 1961). A tool can be made unbiased and result in
optimal resource allocation if payment scheme follows the following procedure. i) sellers
(buyers) are made to report in credible way that unbiased reporting is not profitable, ii) next,
demand and supply can be aggregated to determine the payment level, iii) and this payment level
is applied to individual’s demand and supply of the good. Such payment scheme allows more
than equilibrium level payment calculated at aggregate level to sellers (less than equilibrium
level payment by purchasers), which will automatically work as an incentive for reporting ones
true value. Because this procedure is expensive to be feasible, Vickrey (1961) proposes second
sealed bid can follow this analogy. Vickery’s auction then allows aggregation of information
(Einav et al., 2016) and price determination is based on the second (lowest) highest bid depending
on whether the auction is used for sellers or buyers (Vickrey, 1961).
Furthermore, the properties of sealed bid auctions include the characteristics which allows it to
be incentive compatible that; i) the optimal strategy for bidders (bids placed which is profitable
without loss) by definition is above their own private value (see expression 1), immediately
acting as an incentive to truthful revelation. Thus, over-bidding and under-bidding other than
optimal strategy will not increase the profit of bidders. ii) the bidder knowledge of probability
distribution of ‘other bidders’ and the value they attach to the commodity under consideration is
not needed iii) therefore, in case of information asymmetry the group with less information about
the other bidders does not lose from its unawareness iv) total gain for both sellers and bidders
using this price determination scheme is positive v) the resources allocation is efficient with
additional advantage of less effort and time spent in search of related information since
probability distributions of ‘other bidders’ as well as the their value is irrelevant to the bidder.
Therefore, the bidder has an incentive to reveal the truthful value, the price that one can afford
without incurring loss, in line with optimal strategy shown in the expression 2 (Vickrey, 1961).
B= v (N−1)/N………………………………………………………….. (2)
Where ‘v’ is the value of a product to the bidder (private value), and ‘B’ is bid (asking price)
placed by the bidder, and all other N bidders are expected to bid according this strategy. The
6
equilibrium bidding strategy is incentive compatible and truth revealing because it can be shown
that over bidding and underbidding does not increase the profit of bidders (Vickrey, 1961).
Similar to reverse sealed Vickery auction, UPA and BDM have dominant strategy for truthful
revealing (Becker et al., 1964). BDM differs from second price Vickery auction and UPA in that
price determination in BDM is not based on second lowest price but rather based on random
selection of predetermined prices (Noussair and Van Soest, 2014). Random selection of
predetermined prices also strengthen truthful revelation as bidders cannot influence the price
(Latacz‐Lohmann and Hamsvoort, 1998).
Thus, UPA is an ideal instrument to study WTA in environmental conservation using
representative samples and to suggest the large scale implementation using TILI. Using UPA to
inform TILI will enable us overcome the disadvantage of using TILI alone, which is information
rent. This procedures however is on the assumption that both UPA and TILI can be used as
substitutes or deliver similar results. Studies have documented that outcomes between the two
mechanisms often differ. In this section we first examine if there is a gap and then the
mechanisms for such gap using field experiment. Next section presents the study’s context.
2.3.2 The Study Context
The context of the field experiment is Upper East, Northern and Upper West regions of Ghana.
Ghana is a country that lies in the Sahelian semi-arid belt and has tropical dry forest. The country
also rapidly expanding population, increase in cultivated area, and unsustainable agricultural
practices. These practices in Northern Ghana exert pressure on the diminishing natural resource
base, causing environmental degradation, increased emissions of greenhouse gases, a decrease in
carbon sequestration capacity, and land-use conflicts. For instance, the forest cover of Ghana is
reported to be 15 percent of its land, which is by far lower compared to the land cover of 80
years ago which was about 63 percent of the land (Hackman, 2014). Deforestation rate in Ghana
is alarmingly high like in other tropical countries in which recorde to have remove 90 percent of
primary forest in less than 50 years (FAO, 2010). Land degradation affects agricultural lands,
forests, natural habitats, and water bodies and even exacerbates natural disaster. For instance,
forest land degradation facilitated the widespread of desert which increases the bushfire in the
Ghana. The government of Ghana has attempted to address these problems using different
programmes such as Ghana’s Sustainable Land and Water Management Program (SLWMP).
7
This study attempts to measure effectiveness of SLWMP policy, thus targets communities with
in this programme. Details of sample size determination is presented in the next section.
2.3.3 Sample Size Determination and Power Estimation
The study targets ten communities in the three North regions of Ghana and aims to measure
effectiveness and efficiency of various policies aimed at stimulating the adoption and
maintenance of soil and water conservation technologies under Ghana’s SLWMP. The SLWMP
aims to induce farmers to adopt more sustainable agricultural practices. The environmental
practices promoted by the project should lead to higher yields, improved food security (due to
crop diversification and improved soil quality), improved environmental outcomes (increased
water quality and quantity, carbon sequestration, and soil quality), and ultimately enhanced
quality of life and incomes in the targeted and downstream communities.
In the short run farmers incur costs when adopting and maintaining technologies recommended
by the project. Because the benefits are subtle, not fully understood by local farmers, and only
materialize in the future after costs have been paid, there is an undersupply of environmental
services in the absence of this project. Forty-eight communities were enrolled in the project in
2013-2015.
Out of these communities, at first 10 communities were randomly selected to implement UPA
and TILI price mechanism. However only 6 communities are considered in the later analysis
because of insufficient data within omitted communities (detail of this is presented in analysis
section). Sample size determination include two stage sampling: (i) Village level selection:
randomly selected community which are in the SLWMP project (ii) Household level selection in
which households in communities are randomly selected into the sample without conditioning
that the household is in SLWMP. In each community the sample size is set at maximum of 48
households. The sample power is 0.8. Thus, following the Cohen convention of the minimum
suggested power for ordinary study, our findings have the acceptable level of power (Cohen,
1992).
8
2.3.4 Design and Implementation of Field Experiment
2.3.4.1 DESIGN OF FIELD EXPERIMENT
The field experiment is designed to compare the uptake rate and effectiveness of Payment for
Environmental Services (PES) using two treatments. Originally the PES project in the study area
was implemented in such a way that farmer groups in participating communities can choose to
engage in one of the following two options. Intercropping (maize and leguminous crops), and
tree planting. However, this project does not start to pay the farmers based on service provided.
The current experiment focuses on the project of tree planting and attempt to pay conditional on
service provided. Our tree project provides saplings (mango, cashew, acacia, mahogany, teak),
and farmers are expected to plant 40 per acre. In addition, chicken wire (wire nettings) to protect
saplings, donkey and cart for watering (to be shared within community) is provided to those who
choose to be in tree programme.
The experiment is 2x2 design with two treatment variables to study the take up rate1. These are,
UPA and TILI mechanisms. Subjects are randomly assigned to UPA and TILI group. The
decision to be made in the both treatment groups is on how much they are willing to accept
(WTA) for planting 40 seedlings on their land and keep the trees alive for one year. This decision
is takes into account that the project will provide the subjects with seedlings and other fencing
materials. The implementation of WTA assessments by the two treatments are discussed below.
2.3.4.2 Procedure of Field Experiment
To carry out the experiments in the sample of ten communities, several stages of preparation
were made. First, the extension workers were trained to prepare them take surveys, explain the
TILI and UPA mechanisms, and implement the experiments. Since the communities are
geographically far apart, we divided the communities into two regions (South and East) and
trained the extension workers that work in these two regions (two days each).
Then, the communities were informed to come to the common areas for the experiments by the
extension workers and local administration (community chief). Upon the arrival of subjects at
communal area, the general introduction is made. After that, subjects were informed of activities
to be undertaken and selection criteria into the activities on the field. The criteria used to select
participants into the experiments are; subjects should be 18 years or older, have the decision
1
The effectiveness of the two treatments on output will be studied after a year.
9
power to plant trees on land and preferably owner, only one person per household is allowed to
participate.
After these basic requirements are checked to recruit subjects into the experiment, further
randomization is done as follows. Ten communities were selected in which the sample size in
each community is fixed to the maximum of 48 subjects. Subjects per community were randomly
selected that people who arrive at the meeting place pick numbers in the box and those with
illegible card (numbered 1-48) participate. Next, the subjects go to their respective experiment
groups and the survey administration was made.
Following the administration of survey questions, instruction and explanation for implementing
the experiments (the first task for practice and the main task of tree planting) were given to
subjects. Hence, the tasks include bidding on shirt sell (as a practice) and tree management for
one year (main task). Instruction of experiment centers in directing the subject to think hard of
the value of the good they are placing bid for (see next section). In the same way they were
aware that the price the researcher is willing to pay is pre-determined through random draw
among pool of prices. Next, random draw of each strike prices for shirt sell and tree planting
from two different boxes was made while everyone is watching. Experiment implementation in
UPA and TILI is explained in next section.
2. 3.4.3 Implementation of Field Experiment
In UPA treatment, subjects make decision through bidding amount that is theoretically equal to
their opportunity cost (see expression 1). Based on the bids and a strike price, the status of
joining the programme (PES) is determined. The instructions that subjects follow are designed to
direct the participants’ attention that, it is in their interest to disclose true value. Thus, the
subjects were directed to think hard how much the item worth to them. To further help the
subjects understand UPA mechanism, we carefully designed instructions supported with pictures
of coins in which the role of predetermined price was explained. The pre-determined (strike)
price remained undisclosed during bidding session. Next, before decision making on the main
task using UPA and TILI, subjects practice with unrelated task (shirt sell).
After the practice task, the main experiment was implemented. Seven strike prices and four
virtual prices were implemented in the field experiment to study take up rate variation with
10
prices. The strike prices are the actual contract prices that are used at the end of the experiments.
The virtual prices are random prices that are not actually implemented by the contract, but at the
time of decision making, offered as contract prices to subjects in the TILI group2. As a reference
point, the initial strike price was at first set based on the information that was collected from the
32 extension workers who work closely with farmers. Therefore, these extension workers can be
considered as ‘experts’. Hence, the assumption is that the extension workers can estimate the
strike price that closely relate to the average cost farmers incur to plant the trees and keep it alive
for a year. The experts’ estimation of yearly cost of keeping the trees alive is presented in figure
1. The median value of the experts bid was about 400 GHS ($100) per year.
Thus, 400 GHS is utilized as an initial pre-determined price used in auction experiment. From
400 GHS we vary the prices to study take up variation. Implementation of virtual price is
necessary to further vary the prices from original ‘expert’ valuation. While information from
extensions workers is useful to be used as a reference point, it is clear from literature that experts
tend to overstate the cost of environmental conservation because of the hidden private
information problem (Jack et al., 2009).
Fig 1: Estimated supply curve by Experts
2
Thus, these virtual prices were compared with equal ‘ask prices’ of auction group to drive comparable propensity
to accept under UPA. Hence, for both treatment groups virtual prices were not actually implemented as the price of
the contract.
11
In the TILI treatment group, the strike prices were first revealed individually. Next, subjects
were asked whether they are willing to accept the strike price as a compensation for the costs.
Finally, based on their decisions the status to be in the programme is determined. These strike
prices are the same as with strike prices used in UPA group.
2.4. Result of Field Experiment
2.4.1 The Sample
Table 3 presents the balance of the observables by treatment group. Out of 216 subjects in each
treatment groups (total 432), we utilize 103 in AUP and 93 in TILI (total of 198) for regression
purpose. This is as a result of the 34 excluded because of “implausible numbers” for key
variables and other 28 observation omitted because of missing values.
Table 3: Balance test by treatments
Age
Respondent is female
Land area (acres)
Trees present on land
Access to water point
Distance to fetch Water (km)
Distance to fetch Firewood (km)
Impatience
Risk aversion
N
TILI
UPA
Difference
p-value
42.269
41.657
0.612
0.779
(1.602)
(1.484)
(2.182)
0.344
0.257
0.087
(0.050)
(0.043)
(0.065)
14.296
11.619
2.677
(1.729)
(1.032)
(1.962)
0.968
0.943
0.025
(0.018)
(0.023)
(0.030)
0.172
0.162
0.010
(0.039)
(0.036)
(0.053)
1.018
1.450
-0.432
(0.206)
(0.229)
(0.312)
3.211
2.701
0.510
(0.470)
(0.373)
(0.593)
90.591
82.000
8.591
(11.324)
(9.985)
(15.038)
27.419
27.048
0.372
(2.034)
(1.820)
(2.721)
93
105
198
0.184
0.174
0.404
0.849
0.167
0.391
0.568
0.891
12
The majority of the subjects are men in both treatment groups. Female subjects are on average 30
percent of the total subjects. On average the acres of land owned by the subjects is about 12.96,
and majority plant trees on their land. However, only 16 percent of the subjects on average have
an access to point water sources like bore holes and wells, the rest depend river and rainfall. The
mean distance travelled to fetch water for usage is about 1.23 KMs. Given a small percentage of
population that have access to wells, bore holes and other point water sources, clearly distance
travelled to fetch water correlates with the accessibility of point sources at p<0.05. The mean
distance travelled to collect firewood is 2.97KMs which is longer than mean distance travelled to
fetch water indicating scarcity of firewood. The test of balance between the two experimental
samples show no heterogeneity between the treatment groups in terms of behavioral variables,
that is impatience (time discounting) and risk aversion. The p-values (Table 3) show no
statistical significant difference in for included explanatory variables between the treatment
groups.
2.4.2 Experimental Result: The First Task
In this section we show an overview of results from shirt sell under UPA and TILI mechanisms.
The shirt sell was implemented by setting the predetermined strike prices relatively lower3 to
minimize the researcher costs. This allows feasible, actual transaction. Two points stand out from
this round experiments.
First, the average shirt sell by treatment groups differ. The share of shirt sold in UPA is (0.44)
(94 out of 216) while the share sold under TILI is (0.80) (171 out of 216). Proportion test of
difference shows that the shirt sold under UPA and TILI differs at (p<0.001) level. Individuals
in TILI group sell 1.82 times than those in auction.
Shirt sold under different prices also differ by treatment groups. Second, the test of difference at
all four prices (2GHS, 3GHS, 4GHS, 5GHS) show highly significant difference in shirt sold
between auction and take it -leave-it. For each price, lower number of shirts were sold under
UPA than TILI. Thus, TILI group sell is consistently greater than UPA group sell at all price
level.
3
Since shirt sell is meant to help the participants understand how the two treatments work while making decisions
on tree planting, we minimized the cost of the field experiment implementation by fixing the prices of shirts below
5 cedi.
13
2.4.3 Main Experimental Result: Tree Planting
WTA for afforestation, revealed by UPA group displays auction bid that is an upward sloping
curve. This suggests that the rate of take up rate is higher for higher strike prices as expected (see
fig 2 below). Comparison of results of the two treatments show two main points standout.
Figure 2 : Cumulative Auction bid
First, the overall share of the participants who joined the programme in the TILI and UPA group
is 0.98 and 0.93 respectively (only for the strike prices above 180)4. Thus, the take up rate in
TILI is greater than take up rate of the UPA. The proportion test of difference shows the
difference in take up rate between the two treatments is statistically significant at p <0.05
significant level (0.0038). Thus, we can reject the hypothesis that there is no difference in take
up rates under UPA and TILI at a given price.
4
Price above 180 to 400 is the actual prices of the contracts which we vary to study the take up rates across prices.
We were not allowed to sign contracts below 180, thus we implemented virtual (hypothetical) prices instead.
14
Second, the gap in difference of take up rates widens as the strike prices are set below 180 cedi,
which we called as virtual prices. Table 4 shows take up rates differ between the two treatments
for all virtual prices (10, 30, 60, and 100).
Table 4: Fraction of uptake at different Prices
Strike Prices
Tili
Auction
Difference
P-value
180
0.0229
0.327
0.0011
0.387
0.000
0.201
0.007
0.195
0.0874
Observations
215
0.771
(0.425)
0.461
(0.501)
0.569
(0.497)
0.746
(0.437)
0.805
(0.401)
216
0.165
100
0.936
(0.247)
0.788
(0.415)
0.957
(0.206)
0.947
(0.226)
1 (0)
10
30
60
431
At all virtual prices more subjects in TILI group joined the programme than UPA. The
proportion test shows statistically significant difference in take up rates between TILI and
auction at all price level (at p < 0.05).
The above results confirm the findings of others (Jack, 2013, Berry et al., 2015) that at a given
price, take up rates under posted offer price and UPA significantly differ from each other.
Theory predicts that UPA reveals truthful valuation by bidders under assumption of rationality or
sophistication of bidders’ and risk neutrality (Myerson, 1981). Thus, it is possible that preference
variables including risk aversion might have effect decision making on auctions. The study by
(Berry et al., 2015) indicate that risk aversion is the cause for the gap in sell between BDM and
TILI. Building on these hints, in this study we further hypothesized that, i) because UPA is
affected by behavioral variables than TILI, and ii) more time is needed UPA than in TILI, there
is informed and deliberate decision making in UPA than in TILI. To this end, we examine the
role of behavioral variables on decision making using both mechanisms. Table 5 shows
15
predictors of being in the programme by the two treatments in which the behavioral variables are
included.
Table 5: Predictors of self-selection into PES by treatment group
(1)
In the Program (UPA)
(Probit)
(2)
In the Program (TILI)
(Probit)
0.0132***
0.0144*
(0.00478)
-0.534
(0.386)
0.881*
(0.505)
0.0444***
(0.0127)
1.063***
(0.00759)
-1.305
(0.874)
2.447***
(0.931)
-0.00691
(0.00965)
0.0503
(0.349)
0.0191***
(0.00708)
-0.00203*
(0.00112)
-0.00887***
(0.00324)
-1.302**
(0.637)
-0.484**
(0.247)
0.0963
(0.595)
0.0132
(0.0154)
0.000683
(0.00235)
-0.0105
(0.0205)
-0.375
(0.365)
0.251
(0.287)
-0.133
(0.0887)
-0.0270
(0.101)
-0.0259
(0.0692)
0.00841
(0.0416)
0.0337
(0.00717)
(0.0242)
-1.693***
(0.644)
103
-1.259
(1.456)
89
Virtual Price
Virtual Price
Female
Owns Trees
Land size Owned
Access to Water Point
Age (Years)
Time Preference
Risk aversion
Impulsiveness
Self Determination
Distance to collect
Water (KMs)
Distance to collect
Firewood (KMs)
1.Female#c.RiskLovi
ng
_cons
N
adj. R2
Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01
16
The general expression predicted in Table 5 takes the following form in which with standard
errors are clustered at community level.
(Bij) = α + β1Xi + β2Zi + β3Ʈi + β4Ri + β5Ci + Ƹi
Where Bij is whether the subject i, in the village j is in the program (Yes/No), with subject
specific characteristics Xi, and behavioral related variables Ri, (including risk aversion, time
preference, impatience and self-control) and Ƹi is the error term.
Comparing the predictors of self-selecting oneself into the programme under the two
mechanisms, most of the subject’s observables and behavioral variables are not significant in
TILI. The two variables that seem to predict uptake rate in TILI are price, which is straight
forward, and whether the subjects has trees in her acres of land. Other observables such as
whether the subjects has an access to point water sources (boreholes, wells, etc), are not
significant to be considered as TILI predictors.
Contrary to TILI, more observables in UPA, turned out to be significant. For instance, older
people and those with large land size have higher probability of being allocated into the
programme in UPA. The access to point sources of water increases the probability of being in the
programme. The higher the possibility to have access to the different water sources the lesser the
cost, the more probable that the individual are willing to self- select into the progamme. The
bigger acres of cultivated land ownership increases the probability of allocation.
Furthermore, none of the behavioral variables turned out to be significant in TILI while all of
these variables seem to predict self-selection in UPA. For instance, more patient, risk averse, less
impulsive have higher probability to self-select themselves into the programme thorugh UPA.
The risk aversion is elicited using multiple price lists comparable with the risk aversion measure
implemented in the laboratory following (Holt and Laury, 2002). Hence, standard multiple price
lists is utilized and subjects were presented with two tasks to choose from to gain money. The
first option is lottery with equal chances of winning certain money or nothing, and the second
task is to receive certain fixed money with certainty. The fixed money (in cedi) increases from 0
to 50cedi with 10 cedi step size. The subjects compare between the two options and make their
17
decisions. In the same way impatience (time preference) is measured by amount of money the
subjects are willing to differ from today to 30 days. Other behavioral variables used in this
regressions, impulsiveness and self-determination are measured by four Likert-scale question.
For instance impulsiveness question is framed as: “How much do you agree with this statement:
“When I go to market, I buy things that I did not plan on buying”.
The behavioral variables such as risk aversion remains the significant variable in UPA, but was
not significant to TILI group. To this end, it seems there is more uncertainty in auction group
than in TILI. This is not surprising in the case where non-standard belief of probability exists
(Berry et al., 2015). The discrepancy between UPA and TILI can then be explained by the role of
behavioral predictors. The gap between UPA and TILI shows inability of UPA to predict Take
rates in TILI. UPA inability of predicting the TILI take up rate is therefore can be attributed to
risk aversion of bidders and other behavioral variables. For optimal result of auction risk
neutrality is assumed. If the bidders are risk averse the auction is not able to deliver the optimal
outcome (Maskin and Riley, 1984). This is because the risk averse bidders bear the most risk
since as they are eager bidders, the bid values are not necessarily based on cost of bidders but on
their eagerness.
In this chapter we investigate the hypothesis of informed decision making by examining the
effect of observables and behavioral variables on AUP and TILI. We that the behavioral variable
effect decision making under UPA than TILI. The second hypothesis in support of deliberate
decision making in auction is more time is spent on UPA decision-making. This hypothesis is
ought to be tested in more controlled environment such as laboratory. Accordingly in the next
section we use lab experiment to test the deliberate and informed decision making hypothesis as
explanation for the gap between UPA and TILI.
Conclusion
Theoretically auction provides the ideal situation of proper price determination for PES in
allowing the low risk sellers to self-select into the PES. The alternative and relatively easy TILI
is affected by the information rent. Thus, one plausible and cheapest way of setting payment
level for PES is to utilize UPA for in random and representative samples. The use the result from
UPA to inform TILI on larger scale. However recent studies have found the discrepancies on the
18
theoretical equal UPA and TILI using experimental data. This paper attempt to study the causes
of discrepancy between the two mechanisms. We hypothesized that there is deliberate and
informed decision making in UPA than in TILI. Hence, using field experiment this study
compares the take up rate in UPA than in TILI in which wide gap is find. Price variation does not
remove TILI and UPA group gap in terms output. Detail examination find that, the gap in result
of UPA and TILI is attributed to some behavioral variables in this case; to risk aversion,
impatience and self-determination.
Utilizing TILI to implement conservation programme is straight forward. Compared to UPA, the
mechanism in which TILI operate tend to induce more people to ‘vote’ into the programme. The
hypothesized mechanism is “less deliberate decision making” in which less effort is spent
mentally to undertake the decision. This phenomena is examined in the next chapter in more
controlled setting of lab experiment.
Therefore, decision makers can benefit from these results that UPA is more likely to be driven by
behavioral and non-standard probability beliefs. Whereas, in utilizing TILI it is more likely that
less deliberate effort is made.
3. Laboratory Experiment
3.1 Introduction
To study the role of behavioral variables in explaining the gap between the outcomes of UPA
and TILI in more controlled setting, the lab experiment is implemented. To this end, this section
has two objectives. First, it compares the outcome of UPA group in terms the fraction an item
sold in the laboratory with TILI group. Theoretically UPA and TILI should give similar results
because subjects in the experiment are randomly assigned to both experiment group with
identical random sale price. Second, the role of preferences such as risk aversion, loss aversion
and effort (time) spent on decision making is examined in both mechanisms.
19
3.2 Design of Laboratory Experiment
The laboratory experiment was implemented using student subjects from Tilburg University.
Student subjects were endowed with chocolate bar. At the time of experiment implementation,
the bar was new on the market such that little is known about the brand and taste 5. The chocolate
bars are from a company that is socially engaged that it directly trades with Ghanaian farmers.
The subjects make decision to sell the bar for price not more than 5 euros or keep it otherwise,
utilizing two treatment variables; UPA and TILI. The experiment is implemented in seven
sessions, four sessions for UPA and three session of TILI. Theoretically, similar shares of sales
in the two treatments are expected at a given price.
3.2.1 Set up of Laboratory Experiment
A total of 132 subjects participated in the experiment. Out of this, 73 subjects were in UPA,
while 59 were in TILI treatment group. First, we run two sessions of the UPA treatments. The
auction bids are then accepted or rejected based on random price, which was drawn before
sessions took place. Next, experimenters’ line up all “asks” in the first two sessions of auction
from low to high to identify the price at which 50 percent is willing to sell. Then, the median
price which was 2.30 is used as TILI price in the three TILI experiment sessions. For the UPA
sessions, fractions of bar sell is calculated at 2.30 as well. Finally in UPA, the comparison of
submitted bids to strike price determines whether subjects can keep the bar or sell it back to the
experimenters (see below).
3.2.2 Procedures of Laboratory Experiment
Experimental data was collected from the Tilburg university students attending various school in
May, 2016 and August, 2016. The subjects were contacted via email according to standard
procedures of Tilburg University CentER laboratory recruitment schemes. The list of e-mail was
then extracted from the pool of participants who previously showed interest in participating in
economic experiments, and registered into the email pool of the laboratory.
5
Although the chocolate bars are available in some supermarkets none of the subjects indicated that they knew
the brand. We therefore assume that the price (2.30 euros) is not known to the subjects as this price is also
different from the normal prices in the supermarkets.
20
After arrival, the student subjects were seated at their computers and had a chance to sample test
the chocolate. Next, the UPA (TILI) script was read loud for subjects, while at the same time,
they can also follow the script that was placed on their table before they came to the laboratory.
For instance, the auction script is read as follows6;
In a moment, we will ask you how much you need to receive to be willing to give back your
chocolate bar. The price we will pay, has already been determined; the amount you ask does not
affect that predetermined price. If the amount you ask is lower than the predetermined price, we
will buy back your chocolate bar at the predetermined price. If the amount you ask is higher than
the predetermined price, you will keep your chocolate bar, and you will not receive the
predetermined price. So what amount should you ask for?
Think hard how much the chocolate bar is worth to you – what is the minimum amount of money
you need to receive to be willing to give back your bar (and not feel bad about having to give it
back for that amount). It is in your own interest to submit that minimum amount. Why is this so?
Suppose you ask (much) more than that minimum amount, if the amount you ask is still lower
than the predetermined price, you sell at that price…… but you would also have sold at that
price if you had submitted your minimum amount. If the amount you ask is higher than the
predetermined price, you keep your chocolate bar and that is ok if your true minimum is also
higher than the predetermined price. But you would regret having “asked too much” if the
predetermined price turns out to be higher than your true minimum amount.
The script for TILI group is presented to subjects as follows:
Think hard how much you like the chocolate, and what the minimum amount would need to be
for you to be willing to give back your chocolate bar, and not regret doing so at that amount. In
a moment, we will inform you about the price at which we will back your chocolate bar. Decide
whether you are willing to give back your bar for that price, or not.
Following the first part of the experiment (decision to sell the chocolate bar), subjects undertake
two additional tasks. The first is a lottery task to measure the risk attitude while the second is a
lottery task to measure loss aversion.
6
See appendix for full set of instructions.
21
Risk aversion level is elicited using standard multiple price lists. The lists consists of two options
(risky and certain) to choose from and decide how much money to gain. The first option (option
A) is a lottery (risky option) with equal chances of winning 2 euro’s or nothing. Whereas, the
second option (option B) is a certain fixed amount of euros. The fixed euro amount increases
from 0 to 2 euros with 0.40 cents step size and in total there are 6 choices (see Appendix 2, A).
Thus, the subjects compare between option one and two to decide which one to choose for all six
choices.
Loss aversion level is also elicited by multiple price lists with two options presented to subjects.
The first option (option A) offers a choice between participating in a lottery with equal chance of
winning 2.40 euros and losing some amount of euros. The amount of euros that might be lost
ranges from 0 to 2.40 with 0.40 cents step size. In the second option (option B), subjects have an
option to receive a certain amount of which is 0 euros in all the cases. In total there are eight
choice sets and subjects have to choose between option A and option B (see Appendix 2, B).
After the experiment, the subjects were asked to fill a brief questionnaire. The result of the
experiment are presented in the next section.
2. 3. Result: Laboratory Experiment
From total experiment subjects, about 50 percent were female in both experimental groups.
While on average only half of subjects were from Western Europe including the Dutch students.
Below we present the result of lab experiment that tests the following null hypothesis;
H0: Fractions sold in UPA and TILI are equal
H1: Fractions sold in UPA and TILI are not equal
Figure 3 shows the first glance at the data. The upward sloping supply curve is elicited by UPA
mechanism, while the horizontal line represents TILI strike price. The sale of bar with UPA is
shown by diamond indicator, while outcome of TILI is shown by circle indicator. In theory at the
strike 2.30 euros similar median sell in UPA and TILI expected. However, similar with field
finding, TILI sell diverges for UPA sell, in which larger share of bar sell take place.
22
Figure 3: Supply Curves of Chocolate Sell
0
1
2
3
Price
5
Cumulative of Chocolate Bids
0
.2
.4
.6
.8
1
Density
Note: Horizontal line is the strike price, also TILI offer price; diamond indicator shows the auction sell,
while circle indicates TILI sell.
The share of bar sold under TILI is 0.64 (38 out of 59), while the share sold in UPA is 0.48 (35
out of 73). Fisher exact test shows there is statistical difference between sell in the two
treatments. This result is confirmed using both Pearson Chi2 test and proportion test (Table 2).
Thus, we can reject the null hypothesis that at a given price level take-it-or-leave-it price offer
delivers similar result with the auction.
Table 6: Result of Experimental Test
Sell at
(2.30 euros)
Sell
Observation
TILI
0.64
59
Auction
Mean
difference
0.48
73
0.16
P-value
Fisher
Pearson
Exact
Chi2
0.078
0.059
Proportion
Test
0.066
23
Consistent with field findings, the lab result shows significant disparity similar with other
studies; that the subjects in TILI consistently sell higher than subjects in auctions (Jack, 2013,
Jack et al., 2009, Berry et al., 2015, Cason and Plott, 2014). Here, we have hypothesized, this
disparity might be because of preferences and/or the level of effort required to make decisions
using these mechanisms. The auction mechanism is more complex, and requires more mental
(time) effort than TILI. The intuition is that the subjects in TILI answer question “Is the offered
price high enough?” Which is answered with yes or no. Subjects in auction face uncertainty
about the predetermined price.
The above phenomena suggests that behavioral factors including risk aversion and loss aversion
may be relevant in explaining the difference between the two mechanisms as well. We examine
this phenomena using regressions. Table 7 presents the regression of subjects’ ask price in
auction and decision of TILI group on risk aversion and loss aversion using median split of risk
aversion and loss aversion.
Table 7: Predictors of Decisions
Loss Aversion
Male
Loss Aversion*Male
Not Willing to Taste
West European
(OLS)
Auction Ask
Price
(Probit)
Auction Sell
(Y/N)
(Probit)
TILL Sell
(Y/N)
-0.129
(0.162)
0.500
(0.378)
-0.0129
(0.240)
-0.382
(0.333)
0.696**
(0.353)
0.692
(0.582)
0.900*
(0.353)
-1.082***
(0.360)
0.0843
(0.910)
-0.796
(0.654)
0.379
(0.750)
0.0639
(0.834)
0.0426
(0.165)
-0.149
(0.293)
0.295
(0.205)
N
adj. R2
0.298
(0.274)
0.685**
(0.282)
-0.635
(0.480)
0.272
(0.509)
0.0218
(0.194)
-0.443**
(0.213)
Auction group
_cons
(Probit)
Total Sell
(Y/N)
Accept230
2.613***
(0.209)
71
0.033
Standard errors in parentheses:
*
-0.371***
(0.135)
71
0.045
-0.111
(0.369)
56
0.069
-0.0130
(0.223)
127
0.055
p < 0.10, ** p < 0.05, *** p < 0.01
24
Column 1 of Table 7 shows that male in UPA group bid lower, thus have high probability to sell
than women (Column 1 of Table 7). Whereas, including loss aversion as an interaction with
gender shows loss averse male bid lower which is statistically significant. Thus, loss averse men
have low probability of selling. However, the above results is not significant for TILI group.
In addition, subjects in auction sell less bars and the difference is significant (column 4). Role of
loss aversion is significant on auction group decisions to sell the chocolate bar that male subjects
in auction group who are less loss averse bid lower, which result in higher probability of selling
the chocolate bar (column 4). Thus even though men on average tend sell more than women,
men who are loss averse tend to sell lower than women.
In nutshell, the regression results suggest that subjects’ preference toward loss affects the
decision within auction group than in the TILI group which is statistically significant. On
contrary, for TILI group, it seems that the higher sell does not depend on the preference
parameters and socio-demographic characteristics. Thus, behavioral features seem not so
important.
Given these regression results in relation to TILI group, and need to understand the cause of
variance between the two mechanisms, we further hypothesized that the effort spent as measured
by time spent on decision making might explain the variation. Thus, tested the following null
hypothesis as a cause of disparity.
Ho: Subjects in Auction and TILI spend equal time on decision making
H1: Subjects in Auction and TILI do not spend equal time on decision making
This hypothesis is based on intuition that auction implementation is relatively complex and
requires more time and effort because of the uncertainty involved with predetermined price. In
this regard, it is possibly that subjects in auction might spend more time thinking how valuable
the good they are endowed with to them before decision is made than subjects in TILI group7.
Figure 4 compares the time spent by the subjects in the two treatments on decision making of
selling the chocolate bar. The box plot in the figure suggests that the 50 percent of participants’
7
Even though in the instruction both treatment groups are told to think hard how valuable the good is to them, see
Appendix 1).
25
in auction (the area represented by the box)8 spent greater than the median time spent by TILI
group. Thus, TILI group spend considerably lower time on decision making as compared to
auction group. The rank-sum test indicates that difference in time spend on decision making
between the two treatment groups is highly significant. Hence, we can reject the hypothesis that
there is no difference in time spend between the two mechanisms.
0
20
40
60
80
DecisionTime
100
120
140
Fig 4: Time allocation on Decision making
TILI
AUCTION
Rank-sum test of difference between the two treatments in terms of time spent on decision making is
statistically significant at p< 0.01.
The lab results suggest that the discrepancy in the expected outcomes can be attributed to the low
time spent by TILI group on decision making than auction group. We further analyzed the role of
preference on decisions in both experimental groups and find that loss aversion has an effect on
auction decision making, but not on TILI.
8
The area outside the box but between the lines that enclose the boxes represents the other two quartiles of the
data outside the 50% observation.
26
Conclusion
Theoretically uniform price auction and posted offer price yield similar result in selling or
buying given random allocation of subjects to these mechanisms. However, different studies
have recorded the discrepancies in output between the two mechanisms, which is also confirmed
by this paper. This paper further explores the causes of disparity between the two mechanisms in
which the role of preferences in uniform auction output and role of effort spent is analyzed. We
utilized laboratory experiment to compare the proportion of chocolate bar sold using TILI and
uniform price auction mechanism. This study finds statistically significant gap in fraction of sell
between the two mechanisms. The result suggests that the variation in auction output can be
predicted by preference parameters such as loss aversion, while same does not hold for TILI.
Accordingly, auction outcome gives more information on decision behavior than TILI.
Unable to find much information from these parameter in terms of explaining TILI outcome, we
examined the effort spent by subjects in the two mechanisms. Contrary to time spent on auction
decision making, significantly lower effort is spent on decision making by the TILI group.
Hence, researchers and policy makers can utilize this insight that mechanisms chosen should be
in line with the significance of the effort that should be spent on decision making as well as the
role of behavioral variables on the mechanisms.
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28
Appendix
APPENDIX 1
A: INSTRUCTIONS FOR AUCTION
You are about to participate in an experiment on individual decision making. Before we
start, we would like to ask that you do not communicate with other people during this
session. Please also turn off your mobile phone.
The experiment consists of two parts. The instructions for the first part, Part I, will be read
out aloud now, and you are invited to read along. After completion of Part I of the
experiment, we will continue with Part II. At the end there is a very short survey, and then
the session is completed. The money you will earn in the experiment will be paid to you in
cash in class next week.
INSTRUCTIONS FOR PART I
You receive €4 for being here today. The chocolate bar that you just received, is yours.
PLEASE DO NOT OPEN the chocolate bar until the SESSION is over.
The chocolate bar is made of milk chocolate, and has a fudge and brownie flavor. The
company producing this chocolate is socially engaged, and purchases its cocoa directly
from cocoa farmers in Ghana.
Depending on your decisions in Part I, you can take your chocolate bar home, but you may
also decide to sell it back to the experimenters for a monetary payment.
Please think carefully how much you value the bar. Please taste the sample, and decide
whether or not you like its taste.
The price at which we are willing to buy back the chocolate bar from you, has already been
determined. In a moment, we will ask you to indicate the minimum amount of money you
need to receive to be willing to sell the bar back to us.
If the predetermined price is equal to or higher than the amount you indicated, you sell
your bar, and you will receive the predetermined price.
29
If the predetermined price is lower than the amount you indicated, you keep your bar.
Because the price that we are willing to pay for your chocolate bar has already been
determined, the amount you ask only affects whether you will sell your chocolate bar, or
not. It does not influence the price we will pay. That means that it is in your own interest to
submit your true minimum amount of money you need to receive to be willing to sell your
bar back to us. Consider the following.

If you overstate the amount you need to receive but this amount still turns out to be
lower than the predetermined price, you sell your chocolate bar at the
predetermined price. But you would also have sold your bar at the predetermined
price if you would have submitted the true minimum amount you needed to receive.

If you overstate the amount you need to receive and this amount turns out to be
higher than the predetermined price, you keep your bar. But you will regret having
overstated the amount you need to receive if your true minimum amount is lower
than the predetermined price. You would then have been happy to sell your
chocolate bar at the predetermined price. But because you overstated your amount,
you do not sell you bar.
Similarly, it is not in your interest to understate the amount you need to receive; it is in
your best interest to think about what the least amount of money is that you need to
receive to be willing to give back the chocolate bar, and submit that amount.
Your decision is final. If the amount of money you ask is lower than the predetermined
price, you have sold your chocolate bar. At the end of the session you must hand in your
chocolate bar, and you will receive the predetermined price in class next week. If the
amount of money you ask is equal to or higher than the predetermined price, you will not
receive the predetermined price, and you keep your chocolate bar. So please think carefully
about the minimum amount you need to receive to be willing to sell your chocolate bar.
Are there any questions on this? If so, please raise your hand and ask your question in
private.
30
THE PROCEDURES
In a moment, we will start the software. You will be asked to enter the minimum amount of
money you need to receive to be willing to sell back your chocolate bar. You can enter any
amount between 0.00 and 5.00 Euros. Bids can be in full Euros, but also in tens of euro
cents. For example, you can bid Z Euros and 10 cents, Z Euros and 20 cents, etc., but not Z
Euros and 15 cents, or Z Euros and 16 cents.
After all participants have submitted their bids, the computer will compare the bids of each
participant to the predetermined price. The predetermined price is the same for all
participants in this session.
This completes the description of the first part. Are there any questions? If so, please raise
your hand, and we will answer your question in private.
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B: INSTRUCTIONS FOR TILI
You are about to participate in an experiment on individual decision making. Before we
start, we would like to ask that you do not communicate with other people during this
session. Please also turn off your mobile phone.
The experiment consists of two parts. The instructions for the first part, Part I, will be read
out aloud now, and you are invited to read along. After completion of Part I of the
experiment, we will continue with Part II. At the end there is a very short survey, and then
the session is completed. The money you will earn in the experiment will be paid to you in
cash in class next week.
INSTRUCTIONS FOR PART I
You receive €4 for being here today. The chocolate bar that you just received, is yours.
PLEASE DO NOT OPEN the chocolate bar until the SESSION is over.
The chocolate bar is made of milk chocolate, and has a fudge and brownie flavor. The
company producing this chocolate is socially engaged, and purchases its cocoa directly
from cocoa farmers in Ghana.
Depending on your decisions in Part I, you can take your chocolate bar home, but you may
also decide to sell it back to the experimenters for a monetary payment.
Please think carefully how much you value the bar. Please taste the sample, and decide
whether or not you like its taste.
The price at which we are willing to buy back the chocolate bar from you, has already been
determined. In a moment, we will inform you about the price that we are willing to pay for
your chocolate bar. You are then asked whether you are willing to sell back the bar at this
predetermined price, yes or no.
Note that your decision is final. If you indicated that you are willing to sell back your bar at
the predetermined price, you will be paid that price, and you will have to hand in your bar
at the end of the session. If you indicated that you are not willing to sell back your bar at the
predetermined price, you will not receive that amount and you keep your bar. So after
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having heard the predetermined price that we are willing to pay, please think carefully
whether or not you are willing to sell back your bar at that price, or not.
Are there any questions on this? If so, please raise your hand and ask your question in
private.
THE PROCEDURES
In a moment, we will start the software. You will be informed of the price that we are
willing to pay for your chocolate bar, and you are requested to answer the question
whether you are willing to sell back your bar at this price, or not.
The predetermined price is the same for all participants in this session. This completes the
description of the first part. Are there any questions? If so, please raise your hand, and we
will answer your question in private.
APPENDIX 2
A: Instructions for Risk Preference Measurement
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You are given two options:
Option A is to participate in a game in which we flip a coin. If heads comes up, you receive €
2. If tails comes up, you receive nothing;
Option B gives you some amount of money for certain.
Which of the two options do you prefer?
In each row please choose between Option A and Option B. One of the rows will be
randomly selected and played out for money.
Please click "Next" when you have made your choices for each row. You cannot proceed if
you have not made 6 choices.
Option A
1
2
3
4
5
6
Head pays € 2; Tails pays €
0
Head pays € 2; Tails pays €
0
Head pays € 2; Tails pays €
0
Head pays € 2; Tails pays €
0
Head pays € 2; Tails pays €
0
Head pays € 2; Tails pays €
0
Option B
Choose
You receive € 0.00 for sure
A / B
You receive € 0.40 for sure
A / B
You receive € 0.80 for sure
A / B
You receive € 1.20 for sure
A / B
You receive € 1.60 for sure
A / B
You receive € 2.000 for sure
A / B
B: Instructions for Loss Aversion Measurement
You are given two options
Option A is to participate in a game in which we flip a coin. If heads comes up, you win
money; if tails comes up you lose money;
Option B gives you € 0 for certain.
Which of the two options do you prefer?
In each row please choose between Option A and Option B. One of the rows will be
randomly selected and played out for money.
Please click "Next" when you have made your choices for each row. You cannot proceed if
you have not made 8 choices.
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Option A
1
2
3
4
5
6
7
8
Tails = lose € 0.00; Heads = win
€ 2.40
Tails = lose € 0.40; Heads = win
€ 2.40
Tails = lose € 0.80; Heads = win
€ 2.40
Tails = lose € 1.20; Heads = win
€ 2.40
Tails = lose € 1.60; Heads = win
€ 2.40
Tails = lose € 2.00; Heads = win
€ 2.40
Tails = lose € 2.40; Heads = win
€ 2.40
Tails = lose € 2.80; Heads = win
€ 2.40
Option B
You receive € 0.00 for
sure
You receive € 0.00 for
sure
You receive € 0.00 for
sure
You receive € 0.00 for
sure
You receive € 0.00 for
sure
You receive € 0.00 for
sure
You receive € 0.00 for
sure
You receive € 0.00 for
sure
Choose
A / B
A / B
A / B
A / B
A / B
A / B
A / B
A / B
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